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Learning Tree Pattern Transformations

Authors Daniel Neider , Leif Sabellek , Johannes Schmidt , Fabian Vehlken , Thomas Zeume

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ACM Subject Classification
  • Theory of computation → Complexity theory and logic
  • Computing methodologies → Supervised learning
  • Theory of computation → Database theory
Keywords
  • Tree pattern transformations
  • learning from positive examples
  • computational complexity

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Abstract

Explaining why and how a tree t structurally differs from another tree t^⋆ is a question that is encountered throughout computer science, including in understanding tree-structured data such as XML or JSON data. In this article, we explore how to learn explanations for structural differences between pairs of trees from sample data: suppose we are given a set {(t₁, t₁^⋆),… , (t_n, t_n^⋆)} of pairs of labelled, ordered trees; is there a small set of rules that explains the structural differences between all pairs (t_i, t_i^⋆)? This raises two research questions: (i) what is a good notion of "rule" in this context?; and (ii) how can sets of rules explaining a data set be learned algorithmically?
We explore these questions from the perspective of database theory by (1) introducing a pattern-based specification language for tree transformations; (2) exploring the computational complexity of variants of the above algorithmic problem, e.g. showing NP-hardness for very restricted variants; and (3) discussing how to solve the problem for data from CS education research using SAT solvers.

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Daniel Neider, Leif Sabellek, Johannes Schmidt, Fabian Vehlken, and Thomas Zeume. Learning Tree Pattern Transformations. In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 24:1-24:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICDT.2025.24

Author Details

Daniel Neider
  • TU Dortmund University, Germany
  • Center for Trustworthy Data Science and Security, UA Ruhr, Germany
Leif Sabellek
  • CONTACT Research, Germany
Johannes Schmidt
  • Jönköping University, Sweden
Fabian Vehlken
  • Ruhr University Bochum, Germany
Thomas Zeume
  • Ruhr University Bochum, Germany

Funding

  • Neider, Daniel: Supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant 434592664.
  • Schmidt, Johannes: Partially supported by the Swedish Research Council (VR), grant 2022-03214.
  • Vehlken, Fabian: Supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grants 448468041 and 532727578.
  • Zeume, Thomas: Supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grants 448468041 and 532727578.

Acknowledgements

We are grateful to Florin Manea and Markus Schmid for insightful discussions.

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